AI Open Source · RAG 与检索
qdrant/qdrant
用 Rust 写的向量数据库与搜索引擎，强调单机性能与混合检索能力。支持 HNSW 索引、payload 过滤、稀疏与稠密向量混合查询，适合需要把 embedding 相似度和 结构化条件一起搜的场景，例如带元数据筛选的语义搜索、图像检索。
Qdrant - High-performance, massive-scale Vector Database and Vector Search Engine for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
- Repo
- qdrant/qdrant
- Stars
- ★ 31k
- Language
- Rust
- License
- Apache-2.0
- Last push
- 1d ago
- Created
- 2020-05-30
- Topics
- ai-searchai-search-engineembeddings-similarityhnswhybrid-searchimage-search
- Homepage
- https://qdrant.tech
README
Qdrant (read: quadrant) is a vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points—vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks.
With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
Qdrant is also available as a fully managed Qdrant Cloud ⛅ including a free tier.
<p align="center"> <strong><a href="https://qdrant.tech/documentation/quickstart/">Quick Start</a> • <a href="#agent-skills">Agent Skills</a> • <a href="#clients">Client Libraries</a> • <a href="#demo-projects">Demo Projects</a> • <a href="#integrations">Integrations</a> • <a href="#contacts">Contact</a> </strong> </p>Getting Started
Agent Skills
Qdrant provides a collection of ready-to-use agent skills that bring Qdrant's vector search capabilities directly into your AI coding assistant. Install these skills to empower your agent in making critical engineering decisions for optimal vector search performance, such as quantization, sharding, tenant isolation, hybrid search, model migration, and more.
Client-Server
To experience the full power of Qdrant locally, run the container with this command:
docker run -p 6333:6333 qdrant/qdrant
Note that this starts an insecure deployment without authentication, open to all network interfaces. Please refer to secure your instance.
Now you can connect to the server with any client. For example, using Python:
from qdrant_client import QdrantClient
client = QdrantClient(url="http://localhost:6333")
Before deploying Qdrant to production, be sure to read our installation and security guides.
Clients
Qdrant offers the following client libraries to help you integrate it into your application stack:
- Official:
- Community:
Qdrant Edge
Qdrant Edge is a lightweight version of Qdrant designed for edge devices and resource-constrained environments. Unlike Qdrant Server, which uses a client-server architecture, Qdrant Edge runs inside the application process. Data is stored and queried locally and can be synchronized with a Qdrant server. It offers the same powerful vector search capabilities as the client-server version but with a smaller footprint, making it ideal for applications that require low latency and offline functionality.
To get started with Qdrant Edge from Python or Rust, in
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